This post highlights some of the possible economic implications of the so-called “Fourth Industrial Revolution” — whereby the use of new technologies and artificial intelligence (AI) threatens to transform entire industries and sectors. Some economists have argued that, like past technical change, this will not create large-scale unemployment, as labour gets reallocated. However, many technologists are less optimistic about the employment implications of AI. In this blog post we argue that the potential for simultaneous and rapid disruption, coupled with the breadth of human functions that AI might replicate, may have profound implications for labour markets. We conclude that economists should seriously consider the possibility that millions of people may be at risk of unemployment, should these technologies be widely adopted.

The rise of the robots

Rapid advances in robotics and automation technologies in recent years have coincided with a period of strong growth of lesser-skilled jobs in the UK (see for example Figure 1.7 and Table 1.9 of the Low Pay Commission Spring 2016 Report). There is growing debate in the economics community and academia about whether technological progress threatens to displace a large proportion of these jobs in the longer term. Examples where automation is starting to gain traction internationally include warehousing, haulage, hotels, restaurants and agriculture: all industries which are frequently reported by our Agency colleagues to be heavily dependent on lesser-skilled labour. In the UK, driverless cars are currently being trialled on the roads of Milton Keynes and ‘hands off’ self-driving cars are expected on the motorways in 2018.

Robotics: labour-augmenting or job-destroying?

One view, as outlined in a recent Bank Underground blog (and a follow-on post here), is that technological progress has always been labour-augmenting in the past, and is likely to remain so in future. Thus, as manufacturing productivity has grown and factory jobs shed, the associated increase in GDP per capita has resulted in a net increase in job creation, typically in more labour-intensive service industries. So even if robotics started to displace large numbers of workers, jobs dependent on human traits such as creativity, emotional intelligence and social skills (including teaching, mentoring, nursing and social care for example) may become more numerous.

However, many technologists are not so sure that the next industrial revolution will replicate the past, arguing that the mass adoption of robotics threatens to disrupt many industries more-or-less simultaneously, giving neither the economy — nor society in general — the time to adapt to the changes. Advances in robotics might be such that suddenly, most if not all of the basic human functions entailed in manual labour (assembling, lifting, walking, human interaction, etc) could be carried out more effectively and cheaply by machines — with the advantage of being able to work continually at minimal marginal cost. A recent report by Deloitte concluded that around one-third of jobs in the UK are at “high risk” of being displaced by automation over the next two decades, including losses of over 2 million jobs in retail, 1½ million jobs in transportation and storage, and 1¼ million jobs in health and social care.

It’s different this time?

So how might automation in the Fourth Industrial Revolution differ fundamentally from that in the past, preventing technological progress from being labour augmenting, at least in the short to medium term? Perhaps the main difference is the speed of technological progress and its adoption. The technologist Hermann Hauser argues there were nine new General Purpose Technologies (GPTs) with mass applications in the first 19 centuries AD, including the printing press, the factory system, the steam engine, railways, the combustion engine and electricity. GPTs by definition disrupt existing business models and often result in mass job losses in the industries directly affected. For example, railways initiated the replacement of the horse and carriage, with resultant job losses for coachmen, stable lads, farriers and coach builders. Most of these GPTs took several decades to gain traction, partly because of the large amounts of investment required in plant, machinery and infrastructure. So there was sufficient time for the economy to adapt, thus avoiding periods of mass unemployment.

But the pace of technological progress sped up rapidly since the 19th century. Hermann identifies eight GPTs in the 20th century alone, including automobiles, aeroplanes, the computer, the internet, biotechnology and nanotechnology. Most recent innovations have been scalable much more quickly and cheaply. They have also been associated with the emergence of giant technology corporations — the combined market capitalisation of Apple, Google, Microsoft, Amazon and Facebook is currently about $2½ trillion. The faster these new waves of technology arise and the cheaper they are to implement, the quicker they are deployed, the broader their diffusion, the faster and deeper the rate of job loss and the less time the economy has to adapt by creating jobs in sectors not disrupted by GPTs.

And some technologies are evolving at lightning speed, such as the ongoing exponential increase in computing power. Computers have evolved in the past 40 years or so from initially being merely calculators to having applications that include smartphones and, in conjunction with the internet and big data, driverless cars, robots and the “Internet of Things”.

Looking to the future, how might these new GPTs affect the economy? The retail and distribution sector currently has over five million jobs. In the not too distant future, most consumer goods could be ordered online and delivered by either autonomous vehicles or drones. The warehouses in which the goods are stored could be almost entirely automated. Bricks and mortar stores might largely disappear.

How long before robotics starts to disrupt the economy?

The timing and magnitude of these structural changes to the economy are extremely hard to predict. But the speed at which developed economies adopt robotics technologies is perhaps increased by policies in many countries that seek to reduce income inequality in society, such as increases in minimum wage rates, thereby incentivising R&D and capital expenditure in labour-saving machinery and equipment.

Another factor stimulating global investment in robotics technologies is demographics. Japan has experienced a declining population since 2010, reflecting minimal immigration levels and falling fertility rates since the 1970s. With the population (and labour force) projected to decline by as much as one-fifth over the next 50 years, incentives to invest in automation technology are high. So it is perhaps not surprising that Japan has one of the largest robotics industries in the world, employing over a quarter of a million people. Many types of robot are already commercially available, including humanoid robots, androids, guards and domestic robots, in addition of course to industrial robots. Citizens are increasingly familiar and comfortable interacting with them, including the elderly.

Machine learning/artificial intelligence

It is often argued that robots typically can only perform a finite number of well-defined tasks, ideally in controlled environments. So robots can be used extensively in warehouses or factories, but not to interact intelligently or empathetically with humans as secretaries, vehicle drivers, nurses, care assistants, etc — that is, in service industries where the majority of lesser-skilled jobs are found. Hence, humans might always have an absolute advantage over machines in carrying out many types of work involving cognitive and communication skills.

In fact, technologists are making great strides in developing machines capable of mimicking human intelligence. A computer has recently beaten one of the world’s best players of “Go”. Given that the average game has an almost infinite number of outcomes, the computer must mimic cognitive skills such as intuition and strategy, rather than rely purely on brute force in analysing all plausible move sequences — which is how computers were programmed to beat the world’s chess champions nearly twenty years ago. Researchers are confident that widespread economic applications of AI are not too far away. One such example is facial recognition, which has applications in security etc. A Google AI system called FaceNet was trained on a 260 million image dataset, and achieved 86 percent recognition accuracy using only 128-bytes per face.

Conclusion

There is growing concern in the global tech community that developed economies are poorly prepared for the next industrial revolution. That might herald the displacement of millions of predominantly lesser-skilled jobs, the failure of many longstanding businesses which are slow to adapt, a large increase in income inequality in society, and growing industrial concentration associated with the rapid growth of a relatively small number of multi-national technology corporations.

Economists looking at previous industrial revolutions observe that none of these risks have transpired. However, this possibly under-estimates the very different nature of the technological advances currently in progress, in terms of their much broader industrial and occupational applications and their speed of diffusion. It would be a mistake, therefore, to dismiss the risks associated with these new technologies too lightly.

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Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

6 responses to “Should economists be more concerned about Artificial Intelligence?”

This article lacks an understanding of what AI is, how it works, the structure of the process, of the various architectures. Without understanding these one can’t make considered comments as to the roles and functions and when they will be capable of being automated.
The big data probability architectures that have been in hospitals diagnosing and creating treatment plans for a few years, are quite capable of outstripping any BoE economist. Yet the human BoE economists remain, making blatantly wrong predictions again and again. And here is the rub. However more effective automation and “AI” decision making, some roles have a stickiness towards the human, especially those close to the human wielders of power, or perhaps it would be better to say, those roles which when filled by a human aggrandise those in power, rather like a man with a sword on a horse.

I would perhaps pose the problem of automation of some blue and white-collar jobs. The technologies to do these automations are already in existence, but their execution and roll-out will take a bit of time. From a technologist perspective, we already have the solution in sight.
Now ask yourself: in the present economic low-growth state of the world, how much more unemployment would it take for us to have a serious problem? In transportation, in retail.. I think in the US alone, you could create unemployment (with same participation) equivalent to a deep recession.
Also: even if you dont think its quite so close: the remedy (retraining, education, redistribution, government policy) is almost certain to take a while to debate over, and execute. So another reason to start thinking about it now.

The distinction between complementary and competitive technologies is important here, and is standard in at least some parts of economics. The value of horse labor was increased by many complementary technologies. But internal combustion vehicles were a much more powerful competitive technology and the value of horse labor crashed to below subsistence level.

Modeling the possibility of something like that happening to humans doesn’t require new economics, it just requires using the economics we have, without insisting on the conclusion we want in advance.

When we think clearly about this we realize that the value of humans (unlike horses) has to be based on something other than the market value of their labor.

I believe that one of the main roles of economists is to assess and underline the socioeconomic impact of changes over time. From this perspective, economists should pay attention to the ramifications of the development and commercialization of Artificial Intelligence (AI). Here I am referring particularly to Super AI that is the capacity to totally delegating the command of most human affairs to, at the onset, a self-learning entity that we have created. On the surface, a lot of efficiency can be gained in the current Fourth Industrial Revolution. Automation will transform the manufacturing process, the practice of liberal professions in a way that no one can fathom. This is assuming that the designer of this first machine has it right the first time. On the market of goods and services, the consumers will benefit via the quantity, quality availability of those items. However, their accessibility or affordability is the big hiccup in view of the structural unemployment created. It seems that not enough consideration is given to the use of the incoming high labor supply besides, retraining and early retirement. One can foresee the beginning of more leisure time for those who can afford it.
It all boils down to a vision of the desirable society meaning our modus vivendi in the near future. Technology by its very nature is unstoppable. What then should be done in addition of improving the education system? Perhaps a redistribution of the wealth of the country is in the offing through the taxation framework. The Universal Basic Income under discussion could be a viable option depending on the definition of universal and basic.

It also looks as if the so called “exponential” increase in computing power was a normal phase in the introduction of a new technology that is now slowing rapidly (c.f. the end of Moore’s law, physical limits to processor development), and show similar rates of growth (and deceleration) as other technologies (c.f. the Santa Fe work comparing Moore’s law and Wright’s law.) Needs a more critical perspective on technology uptake.

Whilst difficult to predict the future of work as deep learning, machine learning, AI and Robotic Process Automation replace many existing roles and create new ones, it is essential to plan now. Politically, economically, commercially and culturally. Neither economists or technologists have a monopoly on this. Homo Sapiens are generally resistant to change but the sped of change is quickening and its impact widening. If you think the dissatisfaction that drove Trunp to victory and BREXIT to reality was a major step the trends above will be far more unsettling. BOth Trump supporters and BREXIT voters think that going back to the past will deliver freedom from shrinking rural incomes and jobs deplacement. Think again. Unless Truimp and May find answers the dissatisfaction will swell like a Tsunami

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Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England or its policy committees.

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